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SPEPS Preprint

Author(s): Sanjay Mehrotra, Northwestern University, Evanston
Title: Volumetric center method for stochastic convex programs using sampling
Date of Acceptance: 24.01.2000
Submission Date: 20.01.2000
Series Title: Stochastic Programming E-Print Series
(SPEPS)
Editors: Julie L. Higle; Werner Römisch; Surrajeet Sen
Complete Preprint: pdf (urn:nbn:de:kobv:11-10057165)
ps (urn:nbn:de:kobv:11-10046185)
Keywords (eng): Stochastic Programming, Volumetric Center, Analytic Center, Interior PointMethods, Convex Programming
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Abstract (eng):
We develop an algorithm for solving the stochastic convex program (SCP) by combining Vaidya's volumetric center interior point method (VCM) for solving non-smooth convex programming problems with the Monte-Carlo sampling technique to compute a subgradient. A near-central cut variant of VCM is developed, and for this method an approach to perform bulk cut translation, and adding multiple cuts is given. We show that by using near-central VCM the SCP can be solved to a desirable accuracy with any given probability. For the two-stage SCP the solution time is independent of the number of scenarios.
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